Significant progress has been made towards developing Deep Learning (DL) in Artificial
Intelligence (AI) models that can make independent decisions. However, this progress has …

Abstract Machine learning (ML) algorithms are the basis of many services we rely on in our
everyday life. For this reason, a new research line has recently emerged with the aim of …

Traditional cloud-centric approaches to medical data sharing pose risks related to real-time
performance, security, and stability. Medical and healthcare data encounter challenges like …

Medical image analysis with deep learning techniques has been widely recognized to
provide support in medical diagnosis. Among the several attacks on the deep learning (DL) …

In recent years, the widespread adoption of Machine Learning (ML) at the core of complex IT
systems has driven researchers to investigate the security and reliability of ML techniques. A …

The Internet of Things (IoT) is a sort of network that uses a set of protocols and data-sensing
tools to link anything to the Internet. This type of network allows for data exchange as well as …

Background Community health worker (CHW)-led maternal health programs have
contributed to increased facility-based deliveries and decreased maternal mortality in sub …

Artificial intelligence-based algorithms are widely adopted in critical applications such as
healthcare and autonomous vehicles. Mitigating the security and privacy issues of AI …

In the field of computer vision, active research is conducted to improve model performance.
The successful application of transformer models in computer vision has led to the …

This study tackles the challenges of adversarial corruption in model-based reinforcement
learning (RL), where the transition dynamics can be corrupted by an adversary. Existing …